Local safety cultures of risk and regulation: Workplace safety, individual responsibility, and near miss accidents
Bibliographic record
Abstract
Responsibility in workplace safety is being reconfigured in a similar fashion to other neo-liberal shifts in regulation. The current discourse and interpretation of regulation under neo-liberalism is that governments must encourage, if not demand, that workers take individual responsibility for their own safety while at work. However, the ways in which safety rights and regulations are used are continually evolving through their local application across the broad spectrum of workplace sites. The mechanisms of safety regulations are not fixed; they only appear to be from their written and discursive form. In order to understand the practices of workplace safety rights and regulation we must examine how employees negotiate everyday risk within their local cultures of safety. In this dissertation, I provide an analysis of this evolving nature of individual responsibility within current debates on health and safety compliance. I draw on primary data collected during a five-month ethnographic study of large Canadian industrial factory, a follow-up survey of the same workplace, and analyses of legal schedules containing lists of fines for health and safety violations from the Ontario Court of Justice. A primary finding is that the shift in the labour process to participatory rights has resulted in a diffusion of responsibility over workplace risks and a blurring of responsibility between employers and employees over who is responsible for unsafe conditions found inside the workplace. It was also observed that near miss accidents were ignored in regulatory enforcement. In turn, this lack of attention paid to near misses contributed to a cumulative effect in the normalization of risk surrounding near misses within the local safety cultures across the factory. The findings, in turn, pointed to an alternative theory on why new and young workers are injured at higher rates and the need to further research the development of habitual action when new workers are inserted for the first time into a local safety culture. By studying the process of safety risk and regulation, this dissertation makes visible the mechanisms of regulation and what is normally taken-for-granted in everyday health and safety decision-making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.030 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".